Factors Associated with the Progression of Fibrosis on Liver Biopsy in Alaska Native and American Indian Persons with Chronic Hepatitis C
Bibliographic record
Abstract
BACKGROUND: Various factors influence the development and rate of fibrosis progression in chronic hepatitis C virus (HCV) infection. OBJECTIVES: To examine factors associated with fibrosis in a longterm outcomes study of Alaska Native/American Indian persons who underwent liver biopsy, and to examine the rate of fibrosis progression in persons with subsequent biopsies. METHODS: A cross-sectional analysis of the demographic, inflammatory and viral characteristics of persons undergoing liver biopsy compared individuals with early (Ishak fibrosis score of lower than 3) with those with advanced (Ishak score of 3 or greater) fibrosis. Persons who underwent two or more biopsies were analyzed for factors associated with fibrosis progression. RESULTS: Of 253 HCV RNA-positive persons who underwent at least one liver biopsy, 76 (30%) had advanced fibrosis. On multivariate analysis, a Knodell histological activity index score of 10 to 14 and an alpha-fetoprotein level of 8 ng/mL or higher were found to be independent predictors of advanced liver fibrosis (P<0.0001 for each). When surrogate markers of liver inflammation (alanine aminotransferase, aspartate aminotransferase/alanine aminotransferase ratio and alpha-fetoprotein) were removed from the model, type 2 diabetes mellitus (P=0.001), steatosis (P=0.03) and duration of HCV infection by 10-year intervals (P=0.02) were associated with advanced fibrosis. Among 52 persons who underwent two or more biopsies a mean of 6.2 years apart, the mean Ishak fibrosis score increased between biopsies (P=0.002), with progression associated with older age at initial biopsy and HCV risk factors. CONCLUSIONS: The presence of type 2 diabetes mellitus, steatosis and duration of HCV infection were independent predictors of advanced fibrosis in the present cohort, with significant fibrosis progression demonstrated in persons who underwent serial biopsies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".